Journal of KIBIM (한국BIM학회 논문집)

Korean Institute of Building Information Modeling (한국BIM학회)
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Urban Big Data: Social Costs Analysis for Urban Planning with Crowd-sourced Mobile Sensing Data

도시 빅데이터: 모바일 센싱 데이터를 활용한 도시 계획을 위한 사회 비용 분석

  • Received : 2023.12.05
  • Accepted : 2023.12.19
  • Published : 2023.12.31
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Abstract

In this study, we developed a method to quantify urban social costs using mobile sensing data, providing a novel approach to urban planning. By collecting and analyzing extensive mobile data over time, we transformed travel patterns into measurable social costs. Our findings highlight the effectiveness of big data in urban planning, revealing key correlations between transportation modes and their associated social costs. This research not only advances the use of mobile data in urban planning but also suggests new directions for future studies to enhance data collection and analysis methods.

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References

  1. Bontis, N., Crossan, M. M., Hulland, J. (2002). Managing an Organizational Learning System by Aligning Stocks and Flows, Journal of Management Studies, 39(4), pp. 437-469. https://doi.org/10.1111/1467-6486.t01-1-00299
  2. Camagni, R., Gibelli, M. C., Rigamonti, P. (2002). Urban Mobility and Urban Form: The Social and Environmental Costs of Different Patterns of Urban Expansion, Ecological Economics, 40(2), pp. 199-216. https://doi.org/10.1016/S0921-8009(01)00254-3
  3. Kwan, M. P. (2000). Interactive Geovisualization of Activity-travel Patterns Using Three-dimensional Geographical Information Systems : A Methodological Exploration with a Large Data Set, Transportation Research Part C: Emerging Technologies, 8(1), pp. 185-203. https://doi.org/10.1016/S0968-090X(00)00017-6
  4. Maibach, M., Schreyer, C., Sutter, D., Van Essen, H., Boon, B., Smokers, R., Bak, M. (2008). Handbook on Estimation of External Costs in the Transport Sector. CE Delft Solutions for Environment, Economy and Technology www.ce.nl.
  5. Mayeres, I., Ochelen, S., Proost, S. (1996). The Marginal External Costs of Urban Transport, Transportation Research Part D: Transport and Environment, 1(2), pp. 111-130. https://doi.org/10.1016/S1361-9209(96)00006-5
  6. Rotmans, J., van Asselt, M., Vellinga, P. (2000). An Integrated Planning Tool Forsustainable Cities, Environmental Impact Assessment Review, 20(3), pp. 265-276. https://doi.org/10.1016/S0195-9255(00)00039-1
  7. Shin, D. (2017). Urban Sensing by Crowdsourcing: Analysing Urban Trip Behaviour in Zurich, International Journal of Urban and Regional Research, 40(5), pp. 1044-1060. https://doi.org/10.1111/1468-2427.12416
  8. Tseng, S. C., Hung, S. W. (2014). A Strategic Decision-making Model Considering the Social Costs of Carbon Dioxide Emissions for Sustainable Supply Chain-management, Journal of Environmental Management, 133, pp. 315-322. https://doi.org/10.1016/j.jenvman.2013.11.023
  9. Turvey, R. (1963). On Divergences Between Social Cost and Private Cost, Economica, 30(119), pp. 309-313. https://doi.org/10.2307/2601550
  10. Verhoef, E. (1994). External Effects and Social Costs of Road Transport. Transportation Research Part A: Policy and Practice, 28(4), pp. 273-287. https://doi.org/10.1016/0965-8564(94)90003-5
  11. Walters, A. A. (1961). The Theory and Measurement of Private and Social Cost of Highway Congestion, Econometrica: Journal of the Econometric Society, pp. 676-699.
  12. Zambonelli, F. (2011). Pervasive Urban Crowdsourcing: Visions and Challenges. 2011 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops), Seattle, WA, USA, pp. 578-583.